Project Summary Because of the recent failures in the development of anti-amyloid therapeutic strategies for curing Alzheimer's disease (AD), it is prompt to re-evaluate the existing amyloid cascade hypothesis from all possible aspects. Among all these efforts, biophysical and structural characterizations of ?-amyloid aggregates in in- vitro model systems, especially the works that involve the high-resolution solid-state nuclear magnetic resonance (ssNMR) spectroscopy, provide invaluable information from the fundamental sides. However so far, most of these high-resolution works have been focused on the atomic structures of either amyloid fibrils or non- fibrillar aggregates. Very little high-resolution studies have been performed to directly probe the cellular membrane disruption effects induced by the aggregation process of ?-amyloid peptides, which are potentially associated with the neurotoxicity mechanisms of the ?-amyloid aggregates. A major challenge that prevented the high-resolution studies of ?-amyloid-peptide-induced membrane disruption effects in model systems was the heterogeneity, which usually involved co-existence of multiple membrane disruption pathways with mixed intermediate structures. This proposal attempts to solve this problem by generating model systems with distinct predominant membrane disruption effects. These model systems, which contain different ? amyloid aggregates and phospholipid liposomes, are characterized by distinct time-dependent membrane disruption features and structurally homogeneous endpoints. Therefore, they can be studied individually in terms of their membrane disruption effects using high-resolution ssNMR approaches. The outcomes of this proposal, if successful, will provide crucial insights on the high-resolution molecular interactions between ? amyloid aggregates and membranes that are responsible to the membrane disruption. These information help to explain the neuronal cellular toxicity of ?-amyloid aggregates, which further contribute to the re-evaluation of amyloid cascade hypothesis.